POS Multi-tagging Based on Combined Models

Abstract

In the POS tagging task, there are two kinds of statistical models: one is generative model, such as the HMM, the others are discriminative models, such as the Maximum Entropy Model (MEM). POS multi-tagging decoding method includes the N-best paths method and forward-backward method. In this paper, we use the forward-backward decoding method based on a combined model of HMM and MEM. If P(t) is the forward-backward probability of each possible tag t, we first calculate P(t) according HMM and MEM separately. For all tags options in a certain position in a sentence, we normalize P(t) in HMM and MEM separately. Probability of the combined model is the sum of normalized forward-backward probabilities P norm(t) in HMM and MEM. For each word w, we select the best tag in which the probability of combined model is the highest. In the experiments, we use combined model and get higher accuracy than any single model on POS tagging tasks of three languages, which are Chinese, English and Dutch. The result indicates that our combined model is effective.

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